2016/10/10 by Jobin Wilson, Wilson, Jobin, Ram Mohan +7
Business, Management and Accounting · Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Big Data and Business Intelligence #Digital Libraries (cs.DL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Scientific Computing and Data Management #cs.AI #cs.DL #cs.LG
paper · pdf · doi:10.48550/arxiv.1610.02828
KDD 2016, KDD Cup 2016, Appeared in the KDD Cup Workshop 2016,https://kddcup2016.azurewebsites.net/Workshop
arxiv created 2016/10/10 · openalex publication_date 2016/10/10 · arxiv updated 2016/10/11 · openalex created_date 2016/10/21 · openalex updated_date 2026/07/28
The crux of the problem in KDD Cup 2016 involves developing data mining techniques to rank research institutions based on publications. Rank importance of research institutions are derived from predictions on the number of full research papers that would potentially get accepted in upcoming top-tier conferences, utilizing public information on the web. This paper describes our solution to KDD Cup 2016. We used a two step approach in which we first identify full research papers corresponding to each conference of interest and then train two variants of exponential smoothing models to make predictions. Our solution achieves an overall score of 0.7508, while the winning submission scored 0.7656 in the overall results.